Current fault detection practice for complex electronic equipment involves on-line monitoring of the equipment and has a tendency toward high false-alarm rates. In order to minimize the false-alarm rates, techniques such as duplication, error detection codes, watchdog timers, and consistency and capabilities checks are used.
To isolate a fault and to facilitate corrective action such as replacement of a defective component or adjustment of a malfunction, diagnostic procedures are applied to the equipment while the equipment is either installed in its operating environment or is removed to a repair site. The diagnostic procedures usually involve repeated measurements accomplished by applying stimuli to induce approximate operating conditions. This is followed by an evaluation of the measurements and selection of further tests until either a fault is found or proper functioning of the equipment is ascertained.
The diagnostic procedures described are usually implemented with automatic test equipment, on-line monitoring performance equipment or manually controlled test instrumentation. Automatic test equipment operation is controlled by a test program stored in a mass memory device. Manually controlled instrumentation features a printed or displayed test procedure that guides test personnel through a diagnostic or performance procedure.
Test programs and test procedures as aforenoted share common limitations such as, for example, direct testing being required for verifying performance; testing being viable only for functions whose activity levels are relatively high; and a lack of the capability for assessing and calculating a substantial set of failure symptoms.
Recently "artificial intelligence" has been applied to improve diagnostic procedures. A symptom-based approach, frequently termed "shallow reasoning" is most generally used. This approach bases the association between symptoms and faults on experience rather than on reasoned casual derivation.
The symptom-based approach is only relative to a particular unit or device, and has value where human judgement is the principal knowledge source. Hence, human technical expertise is required to develop a diagnostic procedure. Obviously, this approach is not applicable to: new equipment or equipment still under development; equipment for which knowledge of failure mechanisms and modes is unavailable; complex and functionally dense equipment; and equipment requiring automatic test equipment resources to perform testing in real time.
An alternate approach is specification-based and is frequently referred to as "deep reasoning." This approach has the advantages of artificial intelligence prominence and minimum equipment-dependent knowledge. The specification-based approach solves diagnostic problems by reasoning from the structure and functional behavior of equipment. Structure, in this sense, relates to the relationships of components in the equipment and behavior relates to the input/output behavior of each component. Thus, the composite behavior of the equipment can be derived by propragating individual component behavior via component relationships or connectivity. However, multiple possible behaviors are frequently generated, requiring extensive testing resources. Also, means for biasing behavior from failure modes or funtionally critical perspectives are not provided. Consequently, the specification-based approach requires large numbers of tests in the diagnostic procedure.
The present invention integrates the aforenoted symptom-based and specification-based approaches. The advantages of the herein disclosed integrated approach include reducing the number of diagnostic tests and acquiring and feeding back diagnostic test data to improve the quality of the diagnostic processes.